First task…

Read the syllabus!

• We have a shiny new (developing) website for the course
  • https://michaelzeller.de/course-fr/
• Before our next meeting, your task is to peruse the website
  • especially the Syllabus, Schedule, and Report example pages.

Defining ‘far right’

• Broadly: a noun or adjective referring to phenomena that reject (at least elements of) liberalism and/or democracy, and is characterised by nationalism, exclusionism, xenophobia, strong state, welfare chauvinism, traditional ethics, authoritarianism, and/or populism

Publications on radical right (from Arzheimer)

From Arzheimer’s bibliography: https://www.kai-arzheimer.com/tag/bibliography/

Idea of the research field (Arzheimer, 2022)

Wordfish from Arzheimer’s bibliography: Y-axis - how likely the appearance of a word is in research on far right; x-axis - strength of association of a word with underlying dimension

Structure of the course

Initial class poll

Holidays

Several holidays overlap with our class meetings:

• 1 May (Class 2)
• 29 May (Class 6)
• 19 June (Class 9)

FOR NOW, no make-up classes are planned…

• requires attentive reading and preparation so we can cover the material in the surrounding classes
• possibly adding short recorded lectures…

Aims

• knowledge about past and contemporary far-right political and social activity
• familiarity with breadth of research literature covering the far right
• Capacity to…
  • assess developments in far-right socio-politics, especially by identifying underlying factors and comparing across contexts
  • critically evaluate research on the far right, especially by evaluating the data and methods used
  • gather, assess, and present data about far-right socio-politics

What the course is not…

• not for activism against the far right
  • it’s a seminar, like many others, with real-world implications

Connection to Vorlesung

This seminar is connected to Prof. Dr. Berthold Rittberger’s lecture (Vorlesungsübung: The Political System of the European Union)

• is everyone enrolled in that?
• we will tie in Prof. Rittberger’s content through class discussions

Initial class poll

md`## Poll results (Respondents: ${respondentCount})`

import { liveGoogleSheet } from "@jimjamslam/live-google-sheet";
import { aq, op } from "@uwdata/arquero";

surveyResults = liveGoogleSheet(
  "https://docs.google.com/spreadsheets/d/e/" +
    "2PACX-1vROCUn2oxvBJiHkMnEWrVT3TtycRzm6xqQKW0zvHXTzRLb4ajeCTShXMaghF3jowbqFvaZwY8r5j5NX/" +
    "pub?gid=1685617065&single=true&output=csv",
  10000, 1, 5); // adjust the last number to select all relevant columns 

respondentCount = surveyResults.length;

countsColour = aq.from(surveyResults)
  .select("colour")
  .groupby("colour")
  .count()
  .derive({ measure: d => "Colours" })

// Calculate the maximum count from your dataset
maxCountCol = Math.max(...countsColour.objects().map(d => d.count));

plotColour = Plot.plot({
  marks: [
    Plot.barY(countsColour, {
      x: "colour",
      y: "count",
      fill: "colour"
    }),
    Plot.ruleY([respondentCount], { stroke: "#ffffff99" })
  ],
  color: {
    domain: [
      "red",
      "orange",
      "yellow",
      "green",
      "blue",
      "indigo",
      "violet"
    ],
    range: [
      "#d73027",  // red
      "orange",  // orange
      "yellow",  // yellow
      "forestgreen",   // green
      "blue",  // blue
      "#4B0082",  // indigo
      "#8F00FF"   // violet
    ]
  },
  x: { label: "", tickSize: 0 },  // Customize as necessary
  y: {
    label: "",
    tickSize: 0,
    tickFormat: d => d,
    tickValues: Array.from(
      new Set(countsColour.objects().map(d => d.count))
    ).sort((a, b) => a - b),
    domain: [0, maxCountCol]
  },
  facet: { data: countsColour, x: "measure", label: "" },
  marginLeft: 140,
  style: {
    width: 1350,
    height: 500,
    fontSize: 12,
  }
});

Initial class poll

countsInterest = aq.from(surveyResults)
  // .derive({ interest: d => op.split(d.interest, ", ") }) // not needed here... the interest column contains only a single value per row, but you're treating it as if it could contain multiple values separated by commas. So, when you use op.split(d.interest, ", "), it attempts to split a single value (like "Far-right movements") into an array, which causes incorrect behavior during the plotting process.
  .select("interest")
  // .unroll("interest") // not needed in this poll since multiple options are not allowed --- "This takes the array in the 'interest' field and "unrolls" it. This means that if a person has indicated three interests, this step will create three separate records for that person, each record corresponding to one interest. It effectively flattens the array into individual rows, so you'll have one row per interest per each person.
  .derive({
    interest: d =>
      d.interest === "Far-right parties" ? "parties" :
      d.interest === "Far-right movements" ? "movements" :
      d.interest === "Individuals in the far right" ? "individuals" :
      d.interest === "Studying the far right" ? "studying" :
      d.interest  // fallback in case there are unexpected values
  })
  .groupby("interest")
  .count()
  .derive({ measure: d => "Initial student interests" })
  // .rename({ interest: "part" })

// Calculate the maximum count from your dataset
maxCount = Math.max(...countsInterest.objects().map(d => d.count));

plotInterest = Plot.plot({
  marks: [
    Plot.barY(countsInterest, {
      x: "interest",
      y: "count",
      fill: "interest"
    }),
    Plot.ruleY([respondentCount], { stroke: "#ffffff99" })
  ],
  color: {
    domain: [
      "parties", // "Far-right parties",
      "movements", // "Far-right movements",
      "individuals", // "Individuals in the far right",
      "studying" // "Studying the far right"
    ],
    range: [
      "#d73027",  // Far-right parties
      "forestgreen",  // Far-right movements
      "#91bfdb",  // Individuals in the far right
      "#4575b4"   // Studying the far right
    ]
  },
  x: { 
    label: "",
    tickSize: 0,
    tickFormat: d => d
  },  
  y: {
    label: "",
    tickSize: 0,
    tickFormat: d => d,
    // tickFormat: d => Math.floor(d), // Round down to the nearest whole number
    tickValues: Array.from({ length: maxCount + 1 }, (_, i) => i), // Array of whole numbers [0, 1, ..., maxCount]
    domain: [0, maxCount]
  },
  facet: { data: countsInterest, x: "measure", label: "" },
  marginLeft: 140,
  style: {
    width: 1350,
    height: 500,
    fontSize: 12,
  }
});

Course assessment

Participation

• do the readings and discuss in class
• make note of any questions or comments you have as you go through the readings
• most of the readings are research journal articles
  • more in-depth than book chapters—requires greater concentration
  • consider complementing with a general reader (examples listed at of Syllabus)

Presentation (Referat)

• short (max. 20 mins.) group presentation on a method
• groups, topics, and preferred date due to me by Week 4 (15 May)
  • but there are advantages to booking early…

Essay (Hausarbeit): Data report

• data report on a far-right phenomenon (e.g., voters, a party, protests, violence)
• gather and present data, generate analytical insights
• 2500-3500 words (excluding citations)
• 28 June: short synopsis due. 16 August: full, final report due.
• consider working with data visualisation and analysis software!
• A couple rules apply:
  1. no national-level election data (if you want to study a party/parties, consider EU-level or regional-level or local-level);
  2. no drawing any data from or citing Statista or other data curation services.

Course assessment summary

For the essay and writing in general…

Patriotta, G. (2017). Crafting papers for publication: Novelty and convention in academic writing. Journal of Management Studies, 54(5), 747-759.

Typical class meeting

• Opening question(s)/discussion point(s)
  • e.g., theoretical dilemma, typology forming, item from the news
• Summarising lecture on theory/findings related to week’s topic
  • discussion points
• Discussing the reading(s)
  • highlighting important points (esp. theory and research design)
    • students who read will find discussion more illuminating
• Presentation of further research relevant to week’s topic
• Summative discussion: takeaways, new ideas, lingering questions

Throughout, we will use cases to link theory to real-world events

Office hours and communication

• best to send an email
  • michael.zeller@gsi.uni-muenchen.de
  • m.zeller@lmu.de
• 11.00 - 12.00 on Wednesdays at GSI (Oettingenstr. 67) - Room H105
• or by appointment

Moodle

• Self-enrol: The Far Right in Europe and Beyond
  • access key: “FRpol”
• All course readings are available

Resources

Kai Arzheimer’s website: https://www.kai-arzheimer.com/ and Twitter: @ kai_arzheimer

• Extreme Right Bibliography: https://www.kai-arzheimer.com/extreme-right-western-europe-bibliography/

Cas Mudde’s podcast, Radikaal: https://www.radikaalpodcast.com/ and his Twitter: @ CasMudde

C-REX: https://www.sv.uio.no/c-rex/english/

• Knowing what’s (far) right: A compendium: https://www.sv.uio.no/c-rex/english/groups/compendium/c-rex-compendium-print-version.pdf

Resources

ECPR Extremism & Democracy website: https://standinggroups.ecpr.eu/extremismanddemocracy/

• e-Extreme newsletter: https://standinggroups.ecpr.eu/extremismanddemocracy/newsletter/
• Routledge book series: Extremism and Democracy and Fascism and the Far Right

Anti-Defamation League: https://www.adl.org/

and much, much more

About me

• tell me if you have problems understanding me
  • there’s an accent at work that I tragically cannot shake off
• Ph.D. in political science from Central European University (CEU)
  • specialisation in qualitative methods (incl. QCA, [Bayesian] process tracing), but also quant. text analysis, network analysis
• researcher in funded projects on …
  • violent/banned far-right groups and online content moderation
  • radicalisation, violent extremism, polarisation, and resilience
• current work: militant democracy; far-right activist networks; political violence (in 1970s Northern Ireland; in contemporary Italy); paths to female leadership in Asia; protest and polarisation

About the course

• Q & A

About you! Student introductions

• Information about you, possibly including…
  • Expectations for this course
  • favourite joke
  • Study programme
  • favourite song
  • Country (place) of origin
  • top source for news
  • Background in education/research
  • secret skill
  • Academic/professional aspirations

Concept formation

• concepts are the building blocks of social science research
  • a term denoting an abstract idea

Concept formation

• identifying bounds of concepts
  • See Sartori (1970)
• broadly, three types of concepts in social science

Visualising classical/radial concepts

and family resemblance: no characteristics shared by all (e.g., games)

Classical vs. radial (Collier and Mahon 1993)

Options (according to Mudde)

1. family resemblance

Ideological components

• nationalism
• exclusionism
• xenophobia
• strong state
• welfare chauvinism
• traditional ethics

↓

• nativism
• authoritarianism
• populism

Radical and extreme

Again…

Radical right (populist and non-populist cases): opposition to fundamental values of liberal democracy (radical) and belief in a natural order with inequalities (right)

Extreme right: rejection of democracy (Ignazi’s ‘antisystem’—but that depends on the system)

far right encompasses both these terms

Radical and extreme

From: Arzheimer, Kai. “Conceptual Confusion is not Always a Bad Thing: The Curious Case of European Radical Right Studies.” Demokratie und Entscheidung. Eds. Marker, Karl, Michael Roseneck, Annette Schmitt, and Jürgen Sirsch. Wiesbaden: Springer, 2018. 23-40.

Examples?

What cases are you familiar with?

How should we categorise them and why?

Examples - talking through (party) cases

even trickier with movements?

Points to be aware of?

• concept formation
• measurement
• methods
• descriptive vs. causal inference
• generalisation